docs: def image_feature_engineering(features, feature_tensors_dict):
    """Add a hidden layer on image features.
  
    Args:
      features: features dict
      feature_tensors_dict: dict of feature-name: tensor
    """
    engineered_features = {}
    for name, feature_tensor in six.iteritems(feature_tensors_dict):
      if name in features and features[name]['transform'] == IMAGE_TRANSFORM:
        with tf.name_scope(name, 'Wx_plus_b'):
          hidden = tf.contrib.layers.fully_connected(
              feature_tensor,
              IMAGE_HIDDEN_TENSOR_SIZE)
          engineered_features[name] = hidden
      else:
        engineered_features[name] = feature_tensor
    return engineered_features